Environmental gesture recognition

ABSTRACT

A data-holding subsystem. The data-holding subsystem includes instructions stored thereon that when executed by a logic subsystem in communication with the data-holding subsystem: receive one or more signals, determine a sensor type for each signal of the one or more signals, identify a sensor type specific pattern corresponding to a motion gesture in at least one of the signals, and generate a gesture message based on the motion gesture. The gesture message may be usable by an operating system of a computing device that includes the data-holding subsystem to provide a system-wide function usable by one or more application programs of the computing device to provide an application specific function.

BACKGROUND

A typical computing application program has little or no knowledge ofhow a computing device executing the application program is beinginteracted with in its surrounding environment. More particularly, theapplication typically has little or no knowledge of how a userphysically interacts with the computing device to provide user inputthat controls operation of the application program. This lack ofenvironmental information may cause the application program to behave ina manner that may be unintuitive to a user or that may result inbehavior that does not match the intensions of the user depending on themanner in which the user is providing input to control the computingdevice. This may be particularly true for mobile computing devices.

SUMMARY

Various embodiments related to generating gestures to control operationof a computing device are provided. For example, one disclosedembodiment provides a data-holding subsystem. The data-holding subsystemincludes instructions stored thereon that when executed by a logicsubsystem in communication with the data-holding subsystem: receive oneor more signals, determine a sensor type for each signal of the one ormore signals, identify a sensor type specific pattern corresponding to amotion gesture in at least one of the signals, and generate a gesturemessage based on the motion gesture. The gesture message may be usableby an operating system of a computing device that includes thedata-holding subsystem to provide a system-wide function usable by oneor more application programs of the computing device to provide anapplication specific function.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter. Furthermore,the claimed subject matter is not limited to implementations that solveany or all disadvantages noted in any part of this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an embodiment of a computing system inwhich operation of a computing device may be controlled via gestures.

FIG. 2 is a flow diagram of an embodiment of a method for generatinggestures from sensor signals to control operation of a computing device.

FIG. 3 is a schematic diagram of an embodiment of a mobile computingdevice including a plurality of sensors.

FIG. 4 is a view of a mobile computing device in a first position.

FIG. 5 is a view of the mobile computing device of FIG. 4 rotatedcounterclockwise along a relative X-axis.

FIG. 6 is a view of the mobile computing device of FIG. 4 rotatedclockwise along the relative X-axis.

FIG. 7 is a view of a mobile computing device in a first position.

FIG. 8 is a view of the mobile computing device of FIG. 8 rotatedcounterclockwise along a relative Y-axis.

FIG. 9 is a view of the mobile computing device of FIG. 8 rotatedclockwise along a relative Y-axis.

FIG. 10 is a view of a mobile computing device in a first position.

FIG. 11 is a view of the mobile computing device of FIG. 10 translatedforward along the relative Z-axis.

FIG. 12 is a view of the mobile computing device of FIG. 10 translatedbackward along the relative Z-axis.

FIG. 13 is a view of a mobile computing device in a first position.

FIG. 14 is a view of the mobile computing device of FIG. 13 translatedup along the relative Y-axis.

FIG. 15 is a view of the mobile computing device of FIG. 13 translateddown along the relative Y-axis.

FIG. 16 is a view of a mobile computing device in a first position.

FIG. 17 is a view of the mobile computing device of FIG. 16 translatedright along the relative X-axis.

FIG. 18 is a view of the mobile computing device of FIG. 19 translatedleft along the relative X-axis.

FIG. 19 is a view of a mobile computing device in a first position.

FIG. 20 is a view of the mobile computing device of FIG. 19 rotatedclockwise along the relative Z-axis.

FIG. 21 is a view a gesture graphical user interface.

DETAILED DESCRIPTION

At present, there exists no software systems that can leverage datacollected from sensor devices of a computing device to generatestandardized gestures to control computing device operation that isdevice agnostic and provides an intuitive control experience for a user.Accordingly, the present disclosure is directed to a computing systemthat employs gestures to control at least some operation of a computingdevice on an operating system-wide and/or an application specific level.The gestures may be generated via user actions or environmental changesthat may be detected as input via environmental sensors that providesignals to extensible gesture recognition software. The extensiblerecognition software may generate control commands that may be usable bythe operating system and/or applications of the computing device tocontrol operation.

As used herein a “gesture” may be virtually any suitable environmentalstimulus that is expressive of an intended effect. Such environmentalstimulus may be detected or derived at least in part by sensors.Gestures may be used in addition to or in place of traditional computingdevice input signals, such as for example, those generated by anexplicit user input device that may include a mouse, wheel mouse,keyboard, scroll buttons, optical input, physical switch, etc.

FIG. 1 illustrates an embodiment of a computing system 100. Thecomputing system 100 may include a computing device 102 that may usegestures created by a user to control operation. Computing device 102includes a logic subsystem 104, and a data-holding subsystem 106.Computing system 102 may optionally include a display subsystem and/orother components not shown in FIG. 1.

Logic subsystem 104 may include one or more physical devices configuredto execute one or more instructions. For example, the logic subsystemmay be configured to execute one or more instructions that are part ofone or more programs, routines, objects, components, data structures, orother logical constructs. Such instructions may be implemented toperform a task, implement a data type, transform the state of one ormore devices, or otherwise arrive at a desired result. The logicsubsystem may include one or more processors that are configured toexecute software instructions. Additionally or alternatively, the logicsubsystem may include one or more hardware or firmware logic machinesconfigured to execute hardware or firmware instructions. The logicsubsystem may optionally include individual components that aredistributed throughout two or more devices, which may be remotelylocated in some embodiments.

Data-holding subsystem 106 may include one or more physical devicesconfigured to hold data and/or instructions executable by the logicsubsystem to implement the herein described methods and processes. Whensuch methods and processes are implemented, the state of data-holdingsubsystem 106 may be transformed (e.g., to hold different data).Data-holding subsystem 106 may include removable media and/or built-indevices. Data-holding subsystem 106 may include optical memory devices,semiconductor memory devices, and/or magnetic memory devices, amongothers. Data-holding subsystem 106 may include devices with one or moreof the following characteristics: volatile, nonvolatile, dynamic,static, read/write, read-only, random access, sequential access,location addressable, file addressable, and content addressable. In someembodiments, logic subsystem 104 and data-holding subsystem 106 may beintegrated into one or more common devices, such as an applicationspecific integrated circuit or a system on a chip.

In the illustrated embodiment, the components of the data-holdingsubsystem may represent software constructs. However, in someembodiments, this architecture could combine hardware and softwarecomponents in a large variety of configurations to achieve the samesystem functionality. In some embodiments of a multi-purpose operatingsystem, each of the software components that involve data-holding orstorage may interact with various forms of storage hardware or storagedevice infrastructure provided by the operating system. In someembodiments of a mobile or embedded computing system, some of thefunctionality of components within data-holding subsystem 106 may bedistributed to separate pieces of circuitry and/or integrated into othercomponents illustrated in FIG. 1 as appropriate for the hardwareimplementation. For example, a prepossessing application used to filtersignals generated by a sensor or provide gesture related functionalitymay be executed in firmware of a peripheral sensor device.

In some embodiments, the data-holding subsystem may includecomputer-readable removable media, which may be used to store and/ortransfer data and/or instructions executable to implement the hereindescribed methods and processes.

When included, display subsystem 108 may be used to present a visualrepresentation of data held by data-holding subsystem 106. As the hereindescribed methods and processes change the data held by the data-holdingsubsystem, and thus transform the state of the data-holding subsystem,the state of display subsystem 108 may likewise be transformed tovisually represent changes in the underlying data. Display subsystem 108may include one or more display devices utilizing virtually any type oftechnology. Such display devices may be combined with logic subsystem104 and/or data-holding subsystem 106 in a shared enclosure, or suchdisplay devices may be peripheral display devices.

In some embodiments, the display subsystem may act as a user inputdevice. For example, the display subsystem may include a touch screenconfigured to detect when a user touches the surface of the touchscreen. In some embodiments, the display subsystem may include one ormore light sensors that detect user input in the form of an entityhovering proximate to a surface of the display subsystem.

Continuing with FIG. 1, computing device 102 may include a plurality ofsensors 110 (e.g. sensor 1, sensor 2, sensor N) that may be integratedinto the computing device. The plurality of sensors 110 may beenvironmental sensors configured to measure changes of the computingdevice relative to the surrounding environment (e.g., computing deviceorientation, position, motion, etc.) and/or environmental changesrelative to the computing device (e.g., user input, ambient light,elapsed time duration, entity position, etc.). The plurality of sensors110 may generate signals indicating user input among other environmentalparameters that may be sent to sensor input 112.

Nonlimiting examples of different types of sensors that may be employedmay include an accelerometer, a gyroscope, a camera, a light sensor, anda touch sensor, or virtually any other suitable sensor capable ofmeasuring environmental data. In some embodiments, a sensor may measuretwo or more environmental changes that generate corresponding signals.Further, a sensor may generate two or more separate signals indicativeof environmental changes. For example, an accelerometer maydifferentiate accelerations above a threshold as “jolts”. Theaccelerometer may send separate signals, one indicating generalacceleration, and one indicating “jolts”. In this example, theaccelerometer could be identified as two sensors in the subject figure.

Sensor input 112 may comprise hardware and/or software that enablesignals from the plurality of sensors 110 to be received. For example,the sensor input may include input/output ports that may enable theplurality of sensors to be physically connected. In some embodiments,the sensor input may include wireless technologies that enable wirelesscommunication with some or all of the plurality of sensors. Further,sensor input 112 may be operatively coupled to or in communication withperipheral sensor 114. Peripheral sensor 114 may be located external tocommunication device 102. For example, a peripheral sensor may includean ambient light sensor positioned external to the computing device thatmay detect ambient light in the surrounding environment of the computingdevice. It will be appreciated that, in some cases, the sensor input mayreceive signals from a plurality of peripheral sensors.

Furthermore, computing device may include remote device sensor input 116that may be configured to communicate with remote computing device tosend and/or receive environmental sensor signals that may be processedto facilitate control of computing device 102 and/or remote computingdevice 118. The remote computing device 118 may include remote sensor120 that may be integrated with the remote computing device and maymeasure environmental changes. Further, the remote computing device 118may be in communication with peripheral remote sensor 122 that may belocated external to remote computing device 118 and may measureenvironmental changes proximate to the remote computing device. Thesensor signals generated by remote sensor 120 and peripheral remotesensor 122 may be received by remote device sensor input and may beconsidered when generating control commands that dictate operation ofcomputing device 102.

For example, remote device sensor input 116 may include communicationtechnologies, such as wired or wireless radio communication technologiesto send and/or receive sensor signal data to/from remote computingdevice. In some embodiments, the remote device sensor input may includeshort range transmission technologies (e.g., Bluetooth™). In someembodiments, the remote device sensor input may include long rangetransmission technologies that enable sensor signals to be received fromdevice(s) located substantially remote to the computing device (e.g.,LAN, WAN, etc.).

Furthermore, more than one communication protocol may be combined inorder for the signals to propagate between the remote sensor and thecomputing device. For example, a wireless Ethernet protocol such as802.11b may be used to transfer sensor signals from a remote computingdevice to a wireless router, from which the sensor signals travel overEthernet into a cable modem, from which multiple internet communicationtechnologies (e.g., cable, fiber optic, satellite) may be used to sendthe sensor signals to another host computing device that may share anearby sensor which may be connected via a power-line technology (e.g.X-10) to the computing device.

The sensor input 112 may send sensor signals to sensor drivers 124. Thesensor drivers 124 may be configured to perform at least somepre-processing on the received signals in order to make them suitablefor identification by various software programs of computing device 102.For example, sensor drivers 124 may be configured to performpre-filtering of the received sensor signals to normalize the signalsfor further use. In one particular example, the sensor driver applies alow pass filter to an ambient light sensor signal to so that it may benormalized.

The sensor drivers 124 may send the sensor signal data to a sensorapplication programming interface (API) 126. The sensor API 126 maydistribute the sensor signal data to requesting/subscribing softwareprograms of computing device 102. In particular, sensor API 126 may sendsensor signal data to gesture recognition engine 128 that may beconfigured to interpret the sensor signal data to generate gestures thatmay cause actions in software programs to control operation of thecomputing device.

Furthermore, sensor API 126 may send sensor signal data directly toother subscribing software application programs, such as a plurality ofsoftware applications 138 (e.g., software application 1, softwareapplication 2, software application N). The plurality of softwareapplications 138 may utilize the sensor signal data to performapplication specific operations that need not be related to gestures. Byallowing the sensor signal data to be distributed to differentrequesting software programs, the sensor data may be used to in multipledifferent approaches to provide meaningful information for differentcomputing functions. For example, signal data from a camera type sensormay be used to create photographic images while at the same time mayprovide environmental setting information that may be used along withinformation from other sensors to generate a confidence level for agiven gesture to make gesture detection more reliable. Further, sensorAPI 126 may send sensor signal data directly to operating system (OS)motion services 142. The OS motion services 142 may use knowledge of theavailable sensor devices/capabilities to specifically configure theoperating system and/or associated services.

The sensor API 126 may send sensor signals (or signal data) toextensible gesture recognition engine 128. The extensible gesturerecognition engine 128 may be configured to process the sensor signaldata to generate gesture messages or gesture event notifications thatmay be usable by an operating system of the computing device to providea system-wide function and/or may be usable by one or more applicationsof the computing device to provide an application specific function.Further, extensible gesture recognition engine 128 may receive sensordata from input driver 150 and remote device sensor input 116 that maybe useable to generate gesture messages.

The extensible gesture recognition engine 128 may include a recognitionmodule 130 to process received signals to generate gestures and agesture customization module 132 to facilitate calibration andcustomization of gestures. Recognition module 130 may be configured toreceive one or more signals from the sensor API 126, input driver 150,and/or remote device sensor input 116. The recognition module 130 may beconfigured to determine a sensor type for each signal of the one or moresignals that are received. For example, sensor identificationinformation may be provided by the sensor drivers based on theconfiguration of the sensors with the sensor input. In one example,sensors are identified based on the port of the sensor input to whichthe sensors are connected.

In some embodiments, extensible gesture recognition engine 128 mayinclude a plurality of modules that are specialized for specific typesof sensor hardware. Further, the extensible gesture recognition enginemay be configured to allow for additional recognition modules to beplugged-in for recognition of additional instances of sensors or sensortypes.

By recognizing the types of sensors that provide the signal data, theextensible gesture recognition engine may monitor each signal for sensortype specific patterns that are associated with the sensor typedetermined for that signal. For a given type of sensor signal, eachsensor type specific pattern may correspond to a different gesture.Furthermore, two or more sensor type specific patterns from differentsensor signals that correspond to a composite gesture may becollectively recognized by the extensible gesture recognition engine.The recognition module may maintain a database of sensor type specificpatterns and corresponding gestures/composite gestures. Moreover, sinceavailable sensors and sensor types may be recognized by the recognitionmodule, the framework of the extensible gesture recognition engine maybe general-purpose or device agnostic. In other words, sensor typerecognition may allow for flexibility to recognize gestures fromdifferent combinations of sensors that are made available. Accordingly,standardized gestures may be recognized using different systems havingdifferent sensor configurations.

Furthermore, the use of composite gestures may allow for a largergesture collection and greater extensibility since additional gesturesmay be made available for customization. In particular, the extensiblegesture recognition engine may be configured to recognize sensor typespecific patterns corresponding to customized gestures. The customizedgestures may be defined by third parties such as hardware systemdevelopers, software application developers, users, etc. In particularcustomization module 132 may be configured to accept plug-in support foradditional customized gestures beyond a predefined set of gestures.Further, support for additional sensor devices and associated sensortype specific patterns may be provided to the customization module. Asanother example, additional gestures may be customized via user input(by an advanced user or developer) to a gesture calibration userinterface 2100 (shown in FIG. 21) generated by customization module 132.As such, the extensible gesture recognition engine may be configured torecognize sensor type specific patterns corresponding to customizedgestures. Such extensibility may allow for the extensible gesturerecognition engine to be suitable for use with different computingdevice configurations as well as adaptable to changes or upgrades to theconfiguration of a particular computing device.

Continuing with recognition module 130, for each sensor signal, therecognition module may be configured to, in response to identificationof a sensor type specific pattern, generate a gesture corresponding tothat sensor type specific pattern. As discussed above, in one examplethe pattern is mapped to a corresponding gesture via a databasemaintained by the extensible gesture recognition engine. In otherexamples, heuristics may be used to recognize gestures from sensorsignals. It will be appreciated that virtually any suitable process maybe implemented to convert the set of input signals (i.e., sensorsignals) to output signals (i.e., gesture messages) that correspond to abehavior in an application or operating system shell.

Further, the recognition module may be configured to determine aconfidence level for the gesture of each signal based on agreementbetween the one or more signals. In cases where only one signal isgenerated the confidence level is based on that signal. In cases where aplurality of gestures is generated, the confidence level may be based onthe redundancy of the gestures produced by the different signals. Inother words, the more redundancy in gestures, the higher the confidencelevel. In cases where a gesture is identified by sensors of the sametype, the confidence level is determined in a straightforward manner. Incases where a gesture is identified by sensors of different types (e.g.,webcam+light sensor, or webcam+accelerometer.), the confidence level maybe determined in virtually any suitable manner. For example, sensorsignals may be weighted according to signal clarity and the confidencelevel may be determined based on redundancy of the signals with thesensor signals having a higher weight factoring greater into theconfidence level. As another example, the signals of different sensorstypes may be averaged to produce a single “smoother” signal from a“virtual” sensor and the confidence level may be determined based on thevirtual signal. In such a case, various gesture recognition componentsmay monitor multiple streams of sensor input. By determining aconfidence level based on redundancy between sensor signals, gesturerecognition may be made more accurate and reliable.

In some embodiments, the recognition module may receive a plurality ofsignals over period of time or other duration. The recognition modulemay be configured to determine usage patterns of the plurality signalslearned for the duration. In other words, the recognition module maytrack the behavior of each signal and may leverage the learned behaviorto assess the likelihood of a sensor type specific pattern for onesensor occurring in view of sensor type specific patterns occurring (ornot occurring) for one or more other sensor(s). For example, data from alight sensor may be utilized in combination with monitors of userinteraction (mouse, keyboard, etc) and the system clock and data about alocation to detect that a computing device is only used during certaindaylight hours and the usage pattern is “learned” by the recognitionmodule. Accordingly, if a gesture is generated from a signal of anaccelerometer in the middle of the night, it may be determined to beless likely that the gesture is accurate and the confidence level forthe gesture may be reduced in light of the learned usage pattern.

Once the confidence level is determined for a gesture; the recognitionmodule 130 may be configured to generate a gesture message for thegesture responsive to the confidence level being above a confidencethreshold. In some embodiments, the confidence threshold may bepredetermined. In some embodiments, the confidence threshold may becalibrated based on heuristics applied to the gestures. For example, theconfidence threshold may be calibrated automatically by customizationmodule 132 based on sensor functionality. As another example, theconfidence threshold may be “learned” by monitoring for user correctionof false-positive reports. In one particular example, a user performs a“tilt away” gesture (to scroll down) twice such that the documentscrolls by two pages, and then the user immediately scrolls up one pageby touching the screen. This is interpreted by the recognition module tobe a user correction of a false-positive report that the sensitivity ofthe scroll gesture is set too high. Accordingly, the recognition moduleautomatically lowers the sensitivity of the scroll gesture, and overtime the adjustments accumulate until the system is well tuned.

In some embodiments, to facilitate automatic calibration, customizationmodule 132 may be configured to track input history in data-holdingsubsystem (e.g., memory). Further, a plurality of sensor specificcustomization modules may be in communication with each other so thatknowledge of various ways (i.e., sensor signals) the user wouldaccomplish the same gesture (as well as correct for it by doing thereverse gesture) could be known by each recognition module. Further,user input from input driver 150 may be used to detect user correctionof a false-positive gesture response. Using the data from the inputdriver and each of the sensor specific modules, the false-positivecorrection may be learned resulting in automatic gesture calibration. Insome embodiments, this logic could be encapsulated in a separatelearning module (not shown) of the extensible recognition engine.

As another example, the confidence threshold may be calibrated via userinput to a gesture calibration user interface 2100 (see FIG. 21)generated by customization module 132.

In some embodiments, the customization module may be configured to checkfor degradation of one or more signals responsive to the confidencelevel being below the confidence threshold. For example, a sensor signalmay become noisy due to interference and the sensor may dampen thesignal resulting in the signal not creating the sensor type specificpattern corresponding to the gesture. The extensible gesture recognitionengine may be configured to determine that the signal has been dampenedand thus may temporarily ignore the signal till conditions change.Further, the extensible gesture recognition engine may be configured to,upon detection of degradation of one or more signals, determine a newconfidence level based on agreement between signals that arenon-degraded. Accordingly, gestures may be recognized reliably even inthe event of signal degradation.

Further in some embodiments, the sensor API may accommodate sensorsignal degradation through support for sensor properties like accuracyand error. In other words, the sensor device itself may facilitate thisprocess by exposing a property to the sensor API that reduces the needfor the sensor API or client application or gesture recognition moduleto carefully monitor the incoming sensor signal.

In some embodiments, the extensible gesture recognition engine may beconfigured to delay distribution of the gesture message for control ofthe computing device responsive to the confidence level of the gesturebeing below the confidence threshold. In particular, a confidence levelbelow the confidence threshold may indicate false positiveidentification of a gesture. In order to maintain reliable gesturerecognition, the extensible gesture recognition engine may delaydistribution of the gesture message until a like gesture having aconfidence level above the confidence threshold is subsequentlyidentified.

In some cases, the extensible gesture recognition engine may generatetwo gestures, in other cases the engine may absorb the first gesturebased on historical “learned” behavior that dictates whether to absorbthe weak signal's gesture or whether to send it through along with thestrong signal's gesture. In yet some other cases, the scenario maydictate whether to absorb or not generate the gesture. For example, in ascrolling case it would be suitable to absorb a weak signal and “teach”the user to be more explicit with the physical interaction. In anotherexample with a series of (for example) six light sensors across whichthe user can sweep their hand in midair to indicate scrolling, a weaksignal followed by successively stronger signals may strengthen theconfidence level so that gesture messages may be generated for all sixsensor signals. The latency of the gesture messages based on theconfidence level may make the gesture control more robust since thelikelihood of performing unintended computing operations may be reduced.

As discussed above, extensible gesture recognition engine 128 may enablecustomized gestures to be created as well as additional sensor signalsto be identified and usable for gesture customization and calibrationvia customization module 132. In particular, customization module 132may be configured to integrate customized gestures into a set ofstandardized gestures. The customized gestures may be generated byapplications or services that plug-in or provide gesture customizationdata to the customization module. The customization module may beconfigured to calibrate the confidence threshold used to generategesture messages. The calibration may be performed according tovirtually any suitable method as described above. The customizationmodule may be configured to enable sensor calibration by leveraginginformation from one or more sensor signals.

In some embodiments, customization module may be configured to generatea gesture user interface 2100 (shown in FIG. 21). The gesture userinterface may be configured to permit user input to adjust theconfidence threshold of a gesture, and permit user input to createcustomized gestures based on available signals of different sensorsreceived by the extensible gesture recognition engine. Further, thegesture user interface may be configured to enable sensor calibration byleveraging information from one or more other sensor signals. Thegesture user interface 2100 will be discussed in further detail belowwith reference to FIG. 21.

The extensible gesture recognition engine 128 may produce gesturemessages that may be usable by one or more applications of computingdevice 102 to provide an application specific function. In particular,extensible gesture recognition engine 128 may send gesture messages to agesture message queue (or gesture event notification queue) 134 where agesture API 136 may map application specific functions for a pluralityof subscribing applications (e.g., application 1, application 2,application N) 138 to a specified gesture message. In particular,gesture API 136 may access a database that links application specificfunctions to gesture messages. The database may be updated with mapsthat link customized gestures with application specific functions. Thegesture message queue 134 may allow a plurality of applications tohandle the same motion gesture message without blocking the extensiblegesture recognition engine 128. It will be appreciated that the gesturesmay be handled either synchronously or asynchronously in a non-blockingmanner. It will be appreciated that in some embodiments, one or more ofthe plurality of software applications may at least partially existoutside of the data holding subsystem. For example, a softwareapplication may be a web application executed via a web browsingprogram.

This allows the extensible gesture recognition engine 128 to run out ofprocess from the plurality of applications 138 subscribing to thegesture API 136 resulting in greater computing efficiency. However, insome embodiments, a selected application may leverage the extensiblegesture recognition engine in a specialized manner such that theapplication may host it directly. In this case, an instance of therecognition module and/or the extensible software recognition engine maybe executed by the selected application.

It will be appreciated that gestures may also provide function(application or system-wide) that affects operation of the computingdevice hardware. For example, a clamshell type netbook computing devicemay include a hinge equipped with a small motor and hinge sensor. Withthe lid closed, a user may perform a tilt gesture normally associatedwith scrolling. The extensible gesture recognition engine may receivesignals from an accelerometer indicating the tilt as well as a signalfrom the hinge sensor indicating that the lid is closed. The extensiblegesture recognition engine may leverage the two signals to generate acomposite gesture that provides a function to send a signal to the hingemotor to open the lid of the netbook. Furthermore, the composite gesturemay provide software/UI specific functionality in cooperation with thehardware functionality. For example, upon opening of the lid the gesturemay result in a particular message being displayed.

In another example where gestures provide hardware specific function, auser interface may be displayed responsive to user presence or proximityto the computing device. For example, the computing device may bepositioned on a table showing photographs, and as the user approachesand gets within a range where the user could touch the screen, thedetected proximity of the user may generate a gesture that provideapplication specific functionality to the photograph viewingapplication, such as a command to display user interface controls on thedisplay screen. Further, changes in user proximity, as measured thruinfra-red motion sensor or other environmental sensors, for example,could be interpreted as a gesture and signal the machine to awake fromstandby, open the lid if needed, and display the user interface that waspreviously hidden while the machine was idle.

In some embodiments, where computing device hardware operation isaffected by gesture recognition components of the extensible gesturerecognition and associated architecture be designed in such a way to runoutside of the CPU so that gesture recognition could happen while themachine is a sleep or other mode. It will be appreciated thatdistribution of some or all of the components of the architecture toseparate circuitry to facilitate such a scenario lie within the scope ofthe present disclosure.

Furthermore, extensible gesture recognition engine 128 may sendgenerated gesture messages to operating system (OS) event subsystem 140that may be configured to map the gesture message to system-widefunctions that may control computer operation on a system-wide levelbeyond a particular application. OS event subsystem 140 may send thesystem-wide function to operating system motion services 142 to executethe system-wide function.

The OS motion services 142 may be in communication with sensor API 126so that the OS motion services may take advantage of specializedknowledge of available sensor devices or capabilities. The OS motionservices 142 may be in communication with gesture API 136 to monitor thegesture message queue and/or utilize other functionality provided by thegesture API.

The OS event subsystem 140 may receive explicit user interaction signalsgenerated by pointing device (e.g., mouse, stylus, etc) 146 and/orkeyboard device 148, among other explicit user input devices via inputdriver 150, in such embodiments where these devices are implemented. Insuch embodiments, computing device 102 may receive user input via theseexplicit user input devices as well user input detected by environmentalsensors (e.g., accelerometer, touch sensor) to allow for greater userinput flexibility of the computing device.

Furthermore, in some embodiments, input driver 150 may be incommunication with extensible gesture recognition engine 128. Theextensible gesture recognition engine 128 may be configured to, in somecases, prioritize gestures generated by the explicit input devices overgestures generated by environmental sensors and vice versa. For example,the extensible gesture recognition engine may be configured toprioritize a scrolling gesture generated from sensor signals of atrackpad type explicit user input device over a scrolling gesturegenerated from a change in orientation of a mobile computing device asdetected by an accelerometer. As another example, active input from amouse or keyboard might in some cases cause the extensible gesturerecognition engine to ignore gesture signals originating fromenvironmental sensors. In other cases, active input might influencewhich gesture is generated. An example of the latter cases may includesignals indicating a modifier key (e.g. Shift) from a keyboard device tochange from a slow scroll gesture to a rapid scroll gesture.

In some embodiments, sensors within an explicit user input device may beleveraged by the gesture recognition engine 128. In particular, pointingdevice 146 may include a plurality of sensors (i.e., S1, S2, SN) thatmay generate signals that may be sent to sensor drivers 124 to be usedfor gesture recognition. For example, the plurality of sensors of thepointing device may be infrared sensors that may be as dual-purposeperipheral sensors. Further, keyboard device 148 may include a pluralityof sensors (i.e., S1, S2, SN) that may send signals to sensor drivers124 to be used for gesture recognition. For example, the keyboard devicemay include light sensors in each key of the keypad that may detect anobject (e.g., a hand) passing over the keyboard that may be used togenerate a gesture.

It will be appreciated that the general-purpose nature of the abovedescribed gesture recognition framework may allow for implementation onvirtually any suitable computing device. In particular, the flexibilityto identify types of environmental signals and explicit user inputdevice signals to generate gestures allows for adaptability to differentcomputing software architectures and hardware device configurations.Moreover, the extensibility of the customization modules of theextensible gesture recognition engine may allow for continuedadaptability through the customization of additional gestures andrecognition of additional sensors.

FIG. 2 illustrates a flow diagram of an embodiment of a method forgenerating gestures from environmental/motion sensor signals to controloperation of a computing device. Method 200 begins at 202 where themethod may include receiving one or more signal(s). The one or moresignal(s) may be generated by one or more environmental sensor(s). At204, the method may include determining a sensor type for each of theone or more sensor(s). The sensor type may be used to monitor thesignals for sensor type specific patterns. At 206, the method mayinclude identifying a sensor type specific pattern corresponding to agesture in the one or more signal(s). At 208, the method may includedetermining a confidence level for the gesture based heuristics appliedto the sensor signals such as the one or more signal(s) being inagreement or other heuristics such as learned sensor behavior. At 210,it may be determined if the confidence level is greater than aconfidence threshold. If the confidence level is above the confidencethreshold, the method moves to 212. Otherwise, the confidence level isbelow the confidence threshold and the method returns to otheroperation.

At 212, the method may include generating a gesture message based on thegesture. The gesture message may be usable to provide an applicationspecific function for one or more applications and/or usable to providea system-wide function.

The above described method may be performed to process signal receivedfrom environmental sensors to recognize gestures, in some cases motiongestures, that may be used to provide functionality to control acomputing device. The method may take advantage of data collected fromsensors to provide an intuitive computing device control experience thatmay be applicable to mobile computing device applications that need notreceive user input via a keyboard device or pointing device or whereproviding user input via a keyboard device or pointing device may beless intuitive. Note the method may be equally applicable to non-mobileapplications where for example user proximity is interpreted as agesture.

In some embodiments, the above described method may be tied to acomputing system. As an example, FIG. 1 schematically shows a computingsystem 100 that may perform the above described method. In particular,the above described method may be implemented as instructions stored indata holding subsystem 106 and performed by extensible gesturerecognition engine 128 executable by logic subsystem 104.

FIG. 3 schematically illustrates an embodiment of a mobile computingdevice 300 that may include a plurality of environmental or motionsensors to detect user input, environmental changes relative to themobile computing device, and/or changes of the mobile computing devicerelative to the surrounding environment. Note that the mobile computingdevice is exemplary and is provided as a way of illustrating the conceptof motion and environmental gestures, and the present disclosure is notlimited to facilitating such gestures and not limited to implementationin a mobile device.

The mobile computing device 300 may include a touch screen 302, aplurality of light sensors 304, a camera 306, a gyroscope 308, anaccelerometer 310, and a touch or slide sensor 312. It will beappreciated that in some embodiments the mobile computing device mayinclude additional (or alternative) sensors. In some embodiments, someof the above described sensors may be omitted. In some embodiments, someof the above described sensors may be peripheral sensors incommunication with the mobile computing device.

The touch screen 302 may be configured to detect touch input from a useror a device controllable by a user (e.g., a stylus). Touch screen 302may include a plurality of light sensors 304 that may be configured todetect input proximate to touch screen 302 such as actions performed bya user. The plurality of light sensors 304 may further detectenvironmental conditions proximate to touch screen 302, such as ambientlight conditions, for example.

The camera 306 may be configured to detect environmental conditionsrelative to mobile computing device 300. For example, camera 306 maydetect a position of a feature of a user be (e.g., the user's eyes)controlling mobile computing device 300. The camera 306 may trackchanges in position of the feature of the user that generates a gesture.In one example, a mobile computing device rotation gesture is generatedbased on the type of position change of the feature of the user. Forexample, the eyes of the user as detected by the camera may rotateninety degrees.

The gyroscope 308 may be configured to detect rotation of mobilecomputing device 300 in three dimensions. For example, a rotation ofmobile computing device 300 as detected by gyroscope 308 may generate atilt gesture.

The accelerometer 310 may be configured to detect acceleration of mobilecomputing device 300 in three dimensions. For example acceleration ofmobile computing device 300 in a particular direction as detected byaccelerometer 310 may generate a translation or shake gesture. Asanother example, accelerometer 310 may detect rotation of mobilecomputing device 300 that may be compared to the signal generated bygyroscope 308 to confirm that a tilt gesture has been performed on themobile computing device.

The touch or slide sensor 312 may be configured to detect a positionuser's hand on the touch or slide sensor that may generate a gesture.Further, touch or slide sensor 312 may be configured to detect anorientation of a user's hand grasping mobile computing device 300. Thesignal from touch or slide sensor 312 may be used to generate a gesture.In some cases, the position or orientation of a user's hand as detectedby touch or slide sensor 312 may prevent a gesture from being generatedbased on usage patterns learned for the touch or slide gesture. Forexample, a rotation gesture may be prevented from being generated basedon the way the user is grasping mobile computing device 300. Suchgesture prevention may occur based on the confidence level of thegesture being low (i.e., below the confidence threshold) determinedbased on the learned usage patterns for touch or slide sensor 312. Insome embodiments, touch strips may be located around the entireperimeter and/or rear of the mobile computing device to improve theaccuracy of detection of how a user is holding the mobile computingdevice.

FIGS. 4-20 illustrate example gestures that may be generated responsiveto identification of sensor type specific patterns of variousenvironmental sensors. FIGS. 4-6 illustrate toward/away tilt gesturesthat may provide application specific scrolling functions that may begenerated responsive to identification of sensor type specific patternsof various environmental sensors. FIG. 4 schematically illustrates amobile computing device 400 in a first position. The mobile computingdevice 400 displays a current page of an application, such as a webbrowser, for example. In FIG. 5, mobile computing device 400 isillustrated rotated counterclockwise around a relative X-axis from thefirst position as shown in the right side view of the mobile computingdevice. The rotation may cause the top surface of the mobile computingdevice to move towards the user. The counterclockwise rotation generatesa tilt towards the user gesture that provides an application specificdownward scroll function that causes the currently displayed page toscroll down the page. In FIG. 6, mobile computing device 400 isillustrated rotated clockwise around the relative X-axis from the firstposition as shown in the right side view of the mobile computing device.The rotation may cause the top surface of the mobile computing device tomove away from the user. The clockwise rotation generates a tilt awayfrom the user gesture that provides an application specific upwardscroll function that causes the currently displayed page to scroll upthe page.

In one example, the above described tilt gestures are generatedresponsive to identification of a sensor type specific pattern of asignal produced by a gyroscope sensor. In another example, the abovedescribed tilt gestures are generated responsive to identification of asensor type specific pattern of a signal produced by an accelerometersensor. In yet another example, the above described tilt gestures aregenerated responsive to identification of a sensor type specific patternof a signal produced by a gyroscope sensor and an accelerometer sensor.

FIGS. 7-9 illustrate right/left tilt gestures that may provideapplication specific page navigation functions (e.g., forward, back)that may be generated responsive to identification of sensor typespecific patterns of various environmental sensors. FIG. 7 schematicallyillustrates a mobile computing device 400 in a first position. Themobile computing device 400 displays a current page of an application,such as a web browser, for example. In FIG. 8, mobile computing device400 is illustrated rotated counterclockwise around a relative Y-axisfrom the first position. The counterclockwise rotation generates a tiltright gesture that provides an application specific next page (orforward) function that causes the next page to be displayed. In FIG. 9,mobile computing device 400 is illustrated rotated clockwise around therelative Y-axis from the first position. The clockwise rotationgenerates a left tilt gesture that provides an application specificprevious (or back) function that causes the previous page to bedisplayed.

In some embodiments, the tilt gesture may be selectively generated basedon a user grasping mobile computing device 400 with one hand as detectedby a touch sensor.

FIGS. 10-12 illustrate toward/away translation gestures that may provideapplication specific zoom functions that may be generated responsive toidentification of sensor type specific patterns of various environmentalsensors. FIG. 10 schematically illustrates a mobile computing device 400in a first position. The mobile computing device 400 displays a currentpage of an application. In FIG. 11, mobile computing device 400 isillustrated translated toward a user along a relative Z-axis from thefirst position. The forward translation generates a zoom in gesture thatprovides an application specific enlargement function that causescontent on the currently displayed page to be enlarged. In FIG. 12,mobile computing device 400 is illustrated translated away from the useralong the relative Z-axis from the first position. The backwardtranslation generates a zoom out gesture that provides an applicationspecific reduction function that causes content on the currentlydisplayed page to be reduced.

FIGS. 13-15 illustrate up/down translation gestures that may provideapplication specific editing functions that may be generated responsiveto identification of sensor type specific patterns of variousenvironmental sensors. FIG. 13 schematically illustrates a mobilecomputing device 400 in a first position. The mobile computing device400 displays content on a page. In FIG. 14, mobile computing device 400is illustrated translated up along the relative Y-axis from the firstposition. The up translation generates an upward translation gesturethat provides an application specific select for editing function thatcauses content on the currently displayed page to be highlighted forediting. In FIG. 12, mobile computing device 400 is illustratedtranslated down along the relative Y-axis from the first position. Thedown translation generates a down translation gesture that provides anapplication specific editing function that causes the highlightedcontent on the currently displayed page to be edited. In the illustratedexample, the highlighted content is cut from the currently displayedpage.

It will be appreciated that the up/down translation gesture may bemapped to virtually any suitable editing function. In some embodiments,the edition function may be selected via user input.

FIGS. 16-18 illustrate right/left translation gestures that may providesystem-wide application or task switching functions that may begenerated responsive to identification of sensor type specific patternsof various environmental sensors. FIG. 16 schematically illustrates amobile computing device 400 in a first position. The mobile computingdevice 400 displays a current application. In FIG. 17, mobile computingdevice 400 is illustrated translated right along the relative X-axisfrom the first position. The right translation generates a righttranslation gesture that provides a system-wide function that causes thenext application to be displayed. In FIG. 18, mobile computing device400 is illustrated translated left along the relative X-axis from thefirst position. The left translation generates a left translationgesture that provides a system-wide function that causes the previousapplication to be displayed.

FIGS. 19-20 illustrate rotation gestures that may provide applicationspecific display rotation functions that may be generated responsive toidentification of sensor type specific patterns of various environmentalsensors. FIG. 19 schematically illustrates a mobile computing device 400in a first position oriented horizontally. The mobile computing device400 displays a current page in a landscape layout. In FIG. 20, mobilecomputing device 400 is illustrated rotated clockwise around therelative Z-axis from the first position. The clockwise rotationgenerates a rotate right gesture that provides an application specificdisplay rotation function that causes the currently page of theapplication to be displayed in a portrait layout. In some embodiments,the display layout may be changed responsive to the mobile computingdevice being held in a fixed position after a rotation for apredetermined duration.

In one example, the above described tilt/rotation gestures are generatedresponsive to identification of a sensor type specific pattern of asignal produced by an accelerometer sensor. In another example, theabove described tilt/rotation gestures are generated responsive toidentification of a sensor type specific pattern of a signal produced bya camera sensor based on a change in position of a feature of the useror other proximate feature. In yet another example, the above describedtilt/rotation gestures are generated responsive to identification of asensor type specific pattern of a signal produced by an accelerometersensor and a camera sensor.

In one example, the above described translation gestures are generatedresponsive to identification of a sensor type specific pattern of asignal produced by an accelerometer sensor. In another example, theabove described translation gestures are generated responsive toidentification of a sensor type specific pattern of a signal produced bya camera sensor based on a change in position of a feature of the useror other proximate feature. In yet another example, the above describedtranslation gestures are generated responsive to identification of asensor type specific pattern of a signal produced by an accelerometersensor and a camera sensor.

In some embodiments, the translation gestures may be generatedresponsive to identification of a sensor type specific patternindicating a shake action in which the mobile computing device istranslated in a first direction then a second direction during apredetermined duration. In some embodiments, the translation gesturesmay be responsive to identification of a plurality of shake actions.

It will be appreciated that the gestures described above with referenceto FIGS. 3-20 are exemplary and numerous other gestures may be generatedfrom environmental sensor signals. For example, the above describedgestures may be included in a set of standardized gestures that may beapplied to a mobile computing device. Different sets of standardizedgestures may be applied to different computing device configurations.Further, customized gestures may be added to the standardized set basedon the specific computing device configuration.

FIG. 21 illustrates an embodiment of a gesture user interface 2100 thatmay be generated by customization module 132 of FIG. 1, in someembodiments. The gesture user interface 2100 may display a list 2102 ofcurrently available sensors that may be leveraged to create customizedgestures. The list 2102 of currently available sensors may be generatedby the extensible gesture recognition engine based on determining thesensor type of signals received by the extensible gesture recognitionengine. The gesture user interface 2100 may include a create customizedgesture selector 2104 that may be configured to permit user input tocreate customized gesture based on a available signals of differentsensors received by the extensible gesture recognition engine. Inparticular, create customized gesture selector 2104 may permit userinput to define a sensor specific pattern for one or more signals thatmay be identified to generate the customized gesture. Further, thecreate customized gesture selector 2104 may permit user input to definesensor specific patterns for two or more signal that may be collectivelyidentified to generate a customized composite gesture.

The gesture user interface 2100 may include a calibrate gesture selector2106 configured to permit user input to adjust the confidence thresholdof a selected gesture. In the illustrated example, a confidencethreshold may be adjusted along a meter to adjust the confidencethreshold. In some embodiments, user input may select different aspectsconsidered when determining a confidence level of a selected gesture.Non-limiting examples of aspect that may be selected include learnedusage patterns and the number of redundant gestures used to determine aconfidence level.

The gesture user interface 2100 may include a calibrate sensor selector2108 configured to enable sensor calibration by leveraging informationfrom a one or more other sensor signals. For example, a level of signaldampening to compensate for signal degradation may be adjusted based oninformation from one or more other signals. The calibrate sensorselector 2108 may permit user adjustment to select or weight certainsensor signals over other sensor signals.

The above described gesture user interface may enable a user tocalibrate sensors and customize gestures according to their personalpreference. Accordingly, a user may have a more intuitive userexperience when controlling a computing device using gestures.

In some embodiments, the customization module 132 of FIG. 1 may performthe functionality provided by gesture user interface 2100 automaticallyinstead of (or in addition to) providing gesture user interface 2100. Inparticular, customization module 132 may allow a third party (e.g., anoriginal equipment manufacturer constructing a computing device withcertain sensors included, an independent software vendor building asoftware application to take advantage of certain sensors available in apopular machine in the market, or an independent hardware vendorconstructing a specialized add-on for specialized applications) to addnew sensors to the gesture recognition platform. In other words, thecustomization module may be configured to add a new signal to a list ofavailable sensor signals used for gesture recognition in response toreceiving the new signal from a sensor in communication with theextensible gesture recognition engine.

For example, an accelerometer built by an independent hardware vendormay be configured to generate an acceleration signal and a jolt signal.The customization module may be configured to provide extensible gesturefunctionality responsive to the accelerometer communicating with theextensible gesture recognition engine. In particular, the customizationmodule may identify the sensor type of the signals being received. Insome cases, the customization module may be configured to combine the“raw” acceleration and jolt signals to form a composite signal that maybe calibrated. Further, the customization module may facilitate creationof customized gestures based on the signals from the accelerometersensor. Further still, a confidence threshold for gestures generatedbased on the signals from the accelerometer sensor may be set by thecustomization module. It will be appreciated that the above describedlogic may be integrated into hardware, or may be provided as a softwarepackage that integrates with the gesture API.

The customization module 132 may be configured to define one or morecustomized gestures based on one or more signals from the list ofavailable sensor signals. Customized gestures may be defined by thirdparty software applications or third party hardware devices. Forexample, a third party sensor device may communicate with the computingdevice or be plugged-in to the extensible gesture recognition engine,and software supporting the third party sensor device may provide inputto the customization module to create customized gestures based at leastin part on signals from the third party sensor device.

Furthermore, customization module 132 may be configured to adjust theconfidence threshold for various gestures based on heuristics applied tothe sensors generating the gestures or other related scenarios asdiscussed above. In some embodiments, a third party software applicationor a third party hardware device may provide input that dictates a levelof adjustment of the confidence threshold for a given gesture.

In some embodiments, the above described gesture customization, gesturecalibration, and sensor extensibility functionality provided by thecustomization module may be made available to third party software orthird party hardware via the gesture API (or a separate gestureextensibility API). Further, in some embodiments, the gestureextensibility API may be used in place of the gesture user interface toprovide extensibility functionality to a third party.

It is to be understood that the configurations and/or approachesdescribed herein are exemplary in nature, and that these specificembodiments or examples are not to be considered in a limiting sense,because numerous variations are possible. The specific routines ormethods described herein may represent one or more of any number ofprocessing strategies. As such, various acts illustrated may beperformed in the sequence illustrated, in other sequences, in parallel,or in some cases omitted. Likewise, the order of the above-describedprocesses may be changed.

The subject matter of the present disclosure includes all novel andnonobvious combinations and subcombinations of the various processes,systems and configurations, and other features, functions, acts, and/orproperties disclosed herein, as well as any and all equivalents thereof.

It will be appreciated that the computing devices described herein maybe any suitable computing device configured to execute the programsdescribed herein. For example, the computing devices may be a mainframecomputer, personal computer, laptop computer, portable data assistant(PDA), computer-enabled wireless telephone, networked computing device,or other suitable computing device, and may be connected to each othervia computer networks, such as the Internet. These computing devicestypically include a processor and associated volatile and non-volatilememory, and are configured to execute programs stored in non-volatilememory using portions of volatile memory and the processor. As usedherein, the term “program” refers to software or firmware componentsthat may be executed by, or utilized by, one or more computing devicesdescribed herein, and is meant to encompass individual or groups ofexecutable files, data files, libraries, drivers, scripts, databaserecords, etc. It will be appreciated that computer-readable media may beprovided having program instructions stored thereon, which uponexecution by a computing device, cause the computing device to executethe methods described above and cause operation of the systems describedabove.

It should be understood that the embodiments herein are illustrative andnot restrictive, since the scope of the invention is defined by theappended claims rather than by the description preceding them, and allchanges that fall within metes and bounds of the claims, or equivalenceof such metes and bounds thereof are therefore intended to be embracedby the claims.

1. A data-holding subsystem including instructions stored thereon thatwhen executed by a logic subsystem in communication with thedata-holding subsystem: receive one or more signals; determine a sensortype for each signal of the one or more signals; identify a sensor typespecific pattern corresponding to a motion gesture in at least one ofthe signals; and generate a gesture message based on the motion gesture,the gesture message being usable by an operating system of a computingdevice that includes the data-holding subsystem to provide a system-widefunction usable by one or more application programs of the computingdevice to provide an application specific function.
 2. The data-holdingsubsystem of claim 1, wherein the sensor type includes one or more of anaccelerometer, a gyroscope, a camera, a light sensor, and a touchsensor.
 3. The data-holding subsystem of claim 1, wherein the computingdevice is a mobile computing device and the one or more signals aregenerated by environmental sensors integrated into the mobile computingdevice.
 4. The data-holding subsystem of claim 3, wherein the motiongesture includes a rotation of the mobile computing device along arelative X-axis of the mobile computing device and the applicationspecific function includes a scrolling function.
 5. The data-holdingsubsystem of claim 3, wherein the motion gesture includes a rotation ofthe mobile computing device along a relative Y-axis of the mobilecomputing device and the application specific function includes a changepage function.
 6. The data-holding subsystem of claim 3, wherein themotion gesture includes a rotation of the mobile computing device alonga relative Z-axis of the mobile computing device and the applicationspecific function includes a display rotation function.
 7. Thedata-holding subsystem of claim 3, wherein the motion gesture includes atranslation of the mobile computing device along a relative X-axis ofthe mobile computing device and the system-wide function includes anapplication switching function.
 8. The data-holding subsystem of claim3, wherein the motion gesture includes a translation of the mobilecomputing device along a relative Y-axis of the computing device and theapplication specific function includes an editing function.
 9. Thedata-holding subsystem of claim 3, wherein the motion gesture includes atranslation of the mobile computing device along a relative Z-axis ofthe mobile computing device and the system-wide function includes a zoomfunction.
 10. A computing device comprising: an extensible gesturerecognition engine configured to: receive one or more signals, determinea sensor type for each signal of the one or more signals, monitor eachsignal for sensor type specific patterns that are associated with thesensor type determined for that signal, the sensor type specificpatterns corresponding to different gestures, for each signal, inresponse to identification of a sensor type specific pattern, generate agesture corresponding to that sensor type specific pattern, determine aconfidence level for the gesture of each signal based on heuristicsapplied to the one or more signals, and generate a gesture message forthe gesture responsive to the confidence level being above a confidencethreshold, the gesture message being usable by one or more applicationsof the computing device to provide an application specific function. 11.The computing device of claim 10, wherein the extensible gesturerecognition engine is configured to recognize sensor type specificpatterns corresponding to customized gestures defined by a third partysoftware application or a third party hardware device.
 12. The computingdevice of claim 10, wherein the extensible gesture recognition engine isconfigured to generate a composite gesture responsive to collectiverecognition of two or more sensor type specific patterns correspondingto the composite gesture.
 13. The computing device of claim 10, whereinthe extensible gesture recognition engine is configured to check fordegradation of the one or more signals responsive to the confidencelevel being below the confidence threshold, and upon detection ofdegradation of one or more signals, determine a new confidence levelbased on heuristics applied to non-degraded signals.
 14. The computingdevice of claim 10, wherein at least one of the one or more signals isreceived from a sensor of a remote computing device in communicationwith the computing device.
 15. The computing device of claim 10, whereina plurality of signals is received over a duration, and the extensiblegesture recognition engine is configured to determine usage patterns ofthe plurality of signals learned for the duration, and the confidencelevel is further based on the usage patterns.
 16. The computing deviceof claim 10, wherein the gesture message is usable by an operatingsystem of the computing device to generate a system-wide function. 17.The computing device of claim 10, wherein an application specificfunction is mapped to the gesture message via an application programminginterface.
 18. The computing device of claim 17, wherein the sensor typeincludes one or more of an accelerometer, a gyroscope, a camera, a lightsensor, and a touch sensor.
 19. A computing device comprising: anextensible gesture recognition engine comprising: a recognition moduleconfigured to: receive one or more signals, determine a sensor type foreach signal of the one or more signals, monitor each signal for sensortype specific patterns that are associated with the sensor typedetermined for that signal, the sensor type specific patternscorresponding to different gestures, for each signal, in response toidentification of a sensor type specific pattern, generate a gesturecorresponding to that sensor type specific pattern, determine aconfidence level for the gesture of each signal based on heuristicsapplied to the one or more signals, and generate a gesture message forthe gesture responsive to the confidence level being above a confidencethreshold, the gesture message being usable by one or more applicationsof the computing device to provide an application specific function; anda customization module configured to: add a new signal to a list ofavailable sensor signals used for gesture recognition in response toreceiving the new signal from a sensor in communication with theextensible gesture recognition engine, define one or more customizedgestures based on one or more signals from the list of available sensorsignals, and adjust a confidence threshold for a selected gesture. 20.The computing device of claim 19, wherein the customization module adds,defines, and adjusts responsive to input from a third party softwareapplication or third party hardware device.